1. Field of the Invention
In general, the present invention relates to a method, system and program product for learning computer-generated screens based on user key selections. Specifically, the present invention provides a way to render computer-generated screens/pages for a user based on a user's past history of screens visited.
2. Related Art
As the use of computer networks grows, greater demands are being placed on client-host relationships. For example, it is often the case that a client will communicate with a host to navigate though a sequence of computer-generated screens or pages. Typical examples of hosts include mainframes such as the Telnet 3270 and 5250 servers, which are commercially available from International Business Machines of Armonk, N.Y. In some instances, the client will communicate with the host through a web server. In others, the client will communicate with the host directly. In either scenario, a process known as “screen scraping” often occurs. Specifically, when a user requests a certain screen from the host, screen data will be received. This screen data will then be converted into a format usable by the client. For example, when the client directly communicates with the host, the screen data will be received on the client, and then converted into a GUI format or the like. This distributes the burden of screen scraping to the individual clients. Conversely, when the clients communicate with the host through a web server, the screen data will be converted into HTML or the like on the web server and then sent to the clients. One current product that performs this function on a web server is Host Access Transformation Services (HATS), which is a WebSphere product commercially available from International Business Machines Corp.
Unfortunately, even though communicating through a web server can provide many advantages, the fact that a large number of clients might communicate through a single web server makes scalability and efficiency a problem. Specifically, if the web server has to simultaneously perform screen scraping for multiple clients, an overload condition could occur. To date, no existing solution has been provided that takes advantage of the fact that many users tend to request the same screens over and over again. For example, users who make travel reservations for a company will likely visit the same travel-based screens many times. However, no current system provides a way to recognize the users' navigation pattern, and then use that information to streamline the scraping process.
In view of the foregoing, a need exists for a method, system and program product for learning computer-generated screens based on user key selections. Specifically, a need exists for a system that learns a user's navigation pattern, and then uses that pattern to efficiently render screens in the future. A further need exists for such a system to learn screens based on “aid” keys selected by the user to navigate through the screens.